chapter_2/rectangular_shape_bombing_area.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = 1000\n",
    "y = 600\n",
    "\n",
    "standard_deviation_x = x / 2\n",
    "standard_deviation_y = y / 2\n",
    "\n",
    "number_of_simulation = 1\n",
    "number_of_strike_each_simulation = 20\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of hits = 10\n",
      "Number of miss = 10\n",
      "Number of Strikes on Target 50.0 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(number_of_simulation):\n",
    "    hit = 0\n",
    "    miss  = 0\n",
    "    point_x = []\n",
    "    point_y = []\n",
    "    for i in range(number_of_strike_each_simulation):\n",
    "        normal_random_num = np.random.randn()\n",
    "        current_x = standard_deviation_x*normal_random_num\n",
    "        normal_random_num = np.random.randn()\n",
    "        current_y = standard_deviation_y*normal_random_num\n",
    "        point_x.append(current_x)\n",
    "        point_y.append(current_y)\n",
    "        \n",
    "        if abs(current_x) <= standard_deviation_x and abs(current_y) <= standard_deviation_y :\n",
    "            hit += 1\n",
    "        else:\n",
    "            miss += 1\n",
    "    \n",
    "    print(\"Number of hits =\" ,hit)\n",
    "    print(\"Number of miss =\", miss)\n",
    "    print(\"Number of Strikes on Target\" , round(((hit/number_of_strike_each_simulation)*100) , 2) , \"%\")\n",
    "    \n",
    "    area_x = [-500, 500, 500, -500, -500]\n",
    "    area_y = [-300, -300, 300, 300, -300]\n",
    "\n",
    "    plt.plot(area_x, area_y, color=\"blue\")\n",
    "    plt.scatter(point_x, point_y, color=\"red\")\n",
    "    plt.axis('equal')\n",
    "    plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}